Smoking-related psychosocial beliefs and justifications among smokers in India: Findings from Tobacco Control Policy (TCP) India Surveys
Bibliographic record
Abstract
BACKGROUND: Previous research in high-income countries (HICs) has shown that smokers reduce their cognitive dissonance through two types of justifications over time: risk minimizing and functional beliefs. To date, however, the relationship between these justifications and smoking behaviors over time has limited evidence from low- and middle-income countries. This study examines these of justifications and their relation to quitting behavior and intentions among smoking tobacco users in India. METHODS: The data are from the Tobacco Control Policy (TCP) India Survey, a prospective cohort of nationally representative sample of tobacco users. The respondents include smoked tobacco (cigarettes and bidi) users (n = 1112) who participated in both Wave 1 (W1; 2010-2011) and Wave 2 (W2; 2012-2013) surveys. Key measures include questions about psychosocial beliefs such as functional beliefs (e.g., smoking calms you down when you are stressed or upset) and risk-minimizing beliefs (e.g., the medical evidence that smoking is harmful is exaggerated) and quitting behavior and intentions at Wave 2. FINDINGS: Of the 1112 smokers at W1, 78 (7.0%) had quit and 86 (7.8%) had intentions to quit at W2. Compared to W1, there was a significant increase in functional beliefs at W2 among smokers who transitioned to mixed use (using both smoking and smokeless tobacco) and a significant decrease among those who quit. At W2, smokers who quit held significantly lower levels of functional beliefs, than continuing smokers, and mixed users ((M = 2.96, 3.30, and 3.93, respectively, p < .05). In contrast, risk-minimizing beliefs did not change significantly between the two waves. Additionally, higher income and lower functional beliefs were significant predictors of quitting behavior at W2. CONCLUSION: These results suggest that smokers in India exhibit similar patterns of dissonance reduction as reported in studies from HICs: smokers who quit reduced their smoking justifications in the form of functional beliefs, not risk-minimizing beliefs. Smokers' beliefs change in concordance with their smoking behavior and functional beliefs tend to play a significant role as compared to risk-minimizing beliefs. Tobacco control messaging and interventions can be framed to target these functional beliefs to facilitate quitting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".